METHODS, SYSTEMS, APPARATUS AND ARTICLES OF MANUFACTURE TO MODEL eCOMMERCE SALES

ABSTRACT

Methods, apparatus, systems and articles of manufacture methods, systems, apparatus and articles of manufacture to model ecommerce sales are disclosed. A system to model to eCommerce sales includes a trend identifier to compute commerce metric differences corresponding to products, the commerce metric differences based on first commerce metrics scraped at a first time and second commerce metrics scraped at a second time, a splitter to split the commerce metric differences into a first portion of the commerce metric differences corresponding to a first dataset of eCommerce cooperators, and into a second portion of the commerce metric differences corresponding to a second dataset of eCommerce non-cooperators, a machine learning engine to infer sales data by estimating eCommerce non-cooperators sales based on the second portion of the commerce metric differences, and a sales allocator to estimate sales missing from collected sales data based on the estimate eCommerce non-cooperators sales.

RELATED APPLICATION

This patent arises from a continuation of U.S. Pat. Application No.16/230,069 (now U.S. Pat. 11,449,880), filed on Dec. 21, 2018, whichclaims the benefit of U.S. Provisional Pat. Application No. 62/754,368,which was filed on Nov. 1, 2018. U.S. Pat. Application No. 16/230,069and U.S. Provisional Pat. Application No. 62/754,368 are herebyincorporated herein by reference in their entireties. Priority to U.S.Pat. Application No. 16/230,069 and U.S. Provisional Pat. ApplicationNo. 62/754,368 are hereby claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to eCommerce, and, more particularly,to methods, systems, apparatus and articles of manufacture to modeleCommerce sales.

BACKGROUND

Increasing numbers of products are being purchased online from eCommerceentities (e.g., vendors, retailers, sellers, stores, etc.). An eCommerceentity does not have a conventional physical presence where a consumercan physically go to shop. Instead, for an eCommerce store, a consumerelectronically interacts with an eCommerce website (e.g., over theInternet), application, etc. where the consumer can select products forpurchase, pay for the selected products, and have the purchased productsdelivered to a location specified by the consumer. In some instances, aneCommerce entity may have an associated conventional brick-and-mortarstore where a consumer can physically go to shop.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system to estimate eCommerce salesconstructed in accordance with teachings of this disclosure.

FIG. 2 is a block diagram illustrating an example implementation of theexample sales modeler of FIG. 1 .

FIG. 3 is a flowchart representative of example hardware logic ormachine-readable instructions for implementing the sales modeler of FIG.1 and FIG. 2 to model eCommerce non-cooperators sales.

FIG. 4 is a block diagram illustrating an example implementation of theexample bias reducer of FIG. 1 .

FIG. 5 is a flowchart representative of example hardware logic ormachine-readable instructions for implementing the bias reducer of FIG.1 and FIG. 3 to reduce bias in eCommerce non-cooperators salesestimates.

FIG. 6 is a block diagram illustrating an example implementation of theexample data miner of FIG. 1 .

FIG. 7 is a flowchart representative of example hardware logic ormachine-readable instructions for implementing the data miner of FIG. 1and FIG. 6 to model eCommerce non-cooperators sales data.

FIG. 8 illustrates an example processor platform structured to executethe example machine-readable instructions of FIG. 3 to implement theexample sales modeler of FIGS. 1 and/or 2 , the example machine-readableinstructions of FIG. 5 to implement the example bias reducer of FIGS. 1and/or 4 , and/or the example machine-readable instructions of FIG. 7 toimplement the example data miner of FIGS. 1 and/or 6 .

In general, the same reference numbers will be used throughout thedrawing(s) and accompanying written description to refer to the same orlike parts. Connecting lines or connectors shown in the various figurespresented are intended to represent example functional relationshipsand/or physical or logical couplings between the various elements.

DETAILED DESCRIPTION

Commerce measurement entities measure, for example, the purchases ofproducts, services, etc., and link such sales data with demographicinformation. An example commerce measurement entity is The NielsenCompany (US), LLC, the Applicant of the instant application. Commercemeasurement entities can determine sales based on registered panelmembers. To that end, a commerce measurement entity enrolls people whoconsent to being monitored in a panel. Members of a panel are selectedbased on their demographics so that the panel is representative of agroup of persons, a group of households, a geographic region, etc. Thecommerce measurement entity then monitors those panel members todetermine sales by those panel members. Sales at physical stores where aperson can go physically to shop (a.k.a. brick-and-mortar stores) canlikewise be monitored for sales. While sales data can be obtained fromsome eCommerce entities (e.g., eCommerce cooperators, eCommerceparticipators, etc.), not all eCommerce entities are willing and able toprovide sales data and demographic information. eCommerce cooperatorsare eCommerce entities that have agreed to provide sales data anddemographic information, if available, for their sales to a commercemeasurement entity. A commerce measurement entity can use panel membersales data, brick-and-mortar sales data, and eCommerce cooperators salesdata to estimate sales data and demographic information for a group ofpersons, a group of households, a geographic region, demographiccategories, etc. However, traditional eCommerce measurement techniquescannot incorporate sales data for eCommerce entities that do not providesales data (e.g., an eCommerce non-cooperator, an eCommercenon-participator, etc.). In view of challenges associated with suchtraditional eCommerce measurement techniques, analyst discretion and/orguesswork is applied. However, such discretionary behavior, guesswork,and/or reliance on heuristics produces erroneous and/or otherwise biasresults. Furthermore, panel sizes can result in an under-representation(e.g., partially, wholly, etc.) of recorded purchases from eCommercecooperators and/or eCommerce non-cooperators. Thus, panel size canresult in statistical biases in the composition of a panel and, thus,statistical biases in sales data recorded by the panel. Accordingly,traditional eCommerce sales estimates based on panels can fail tocapture all aspects of eCommerce sales (e.g., all eCommerce entities,all products, etc.). Therefore, marketing, advertising, sales, etc.decisions made by manufacturers, advertisers, marketing entities,eCommerce entities, etc. based on recorded panel purchases can beincorrect, resulting in, for example, money spent to advertise productsto the wrong groups of people.

To estimate more statistically accurate (e.g., less bias, lessdiscretionary input (error), without relying on heuristics or guesswork,etc.) and more complete (e.g., representative, comprehensive,encompassing, etc.) sales data for eCommerce non-cooperators in a mannerdevoid of analyst discretion, guesswork and/or bias, some disclosedexample commerce measurement entities obtain commerce metrics thatrepresent information related to aspects of the sales of products.Example commerce metric include publicly available product information(e.g., consumer ratings, consumer comments, consumer questions, etc.)from eCommerce non-cooperators websites. In some examples, the publiclyavailable product information is obtained using web scraping (e.g., webharvesting, web data extraction, etc.). Some disclosed example commercemeasurement entities use the publicly available product information toinfer (e.g., estimate, model, ascertain, project, etc.) sales for theeCommerce non-cooperators. Additionally, and/or alternatively, sales foreCommerce non-cooperators can be inferred (e.g., estimated, modeled,ascertained, projected, etc.) from panel member sales data and/oreCommerce cooperators sales data. Having thus determined sales foreCommerce non-cooperators, the eCommerce non-cooperators sales can becombined with eCommerce cooperators sales data to estimate totaleCommerce sales. Additionally, and/or alternatively, in some examples,bias in sales estimates are removed in a manner devoid of analystdiscretion, guesswork and/or bias. Therefore, the examples disclosedherein provide methods, systems, apparatus and articles of manufactureto model eCommerce sales that are more accurate, more statisticallysignificant and more computationally efficient. Therefore, the examplemethods, systems, apparatus and articles of manufacture disclosed hereinto model eCommerce sales are more computationally efficient thantraditional methods by accurately providing unbiased sales estimatesthat reflect a full range of products and strata, without having torecruit, manage and pay for large numbers of panel members.

Reference will now be made in detail to non-limiting examples, some ofwhich are illustrated in the accompanying drawings.

FIG. 1 illustrates an example system 100 to estimate eCommerce salesconstructed in accordance with teachings of this disclosure. To collecteCommerce cooperators sales data G* 102, and panel sales data 104, theexample system 100 includes an example data collector 106. The exampledata collector 106 of FIG. 1 periodically and/or aperiodically obtains(e.g., collects, queries for, receives, prompts for, etc.) the eCommercecooperators sales data G* 102 from eCommerce cooperators (one of whichis designated at reference numeral 108), and the panel sales data 104from panel members (one of which is designated at reference numeral110). In some examples, the eCommerce cooperators sales data G* 102 andthe panel sales data 104 are a list of purchased items including itemdetails (e.g., description, category, etc.). In some examples, the datacollector 106 scrapes eCommerce websites for commerce metrics.

To estimate eCommerce non-cooperators sales sE_(hp) 112, the examplesystem 100 includes an example sales modeler 114. The example salesmodeler 114 of FIG. 1 estimates the eCommerce non-cooperators salessE_(hp) 112 from the eCommerce cooperators sales data G* 102 and thepanel sales data 104. For different strata h (e.g., city, region, class,age, income, etc.), the sales modeler 114 determines a respectiveexpansion factor xF_(h) 116 based on the number of eCommerce cooperatorssales G_(h) 118 recorded for the strata h, and the number of panel salesg_(h) recorded for eCommerce cooperators recorded for the strata h. Thesales modeler 114 estimates eCommerce non-cooperators sales sE_(hp) 112for a strata h and a product p (e.g., corresponding to a strata h and aproduct p) based on the expansion factor xF_(h) 116, and panel saless_(hp) 120 for eCommerce non-cooperators recorded for the strata h andthe product p. The expansion factor xF_(h) 116 represents the amount bywhich panel sales data 104 for eCommerce cooperators would have to beincreased to be the same amount of sales as the eCommerce cooperatorssales data G* 102. The expansion factor xF_(h) 116 is subsequently usedto increase panel sales s_(hp) 120 for eCommerce non-cooperators to forman estimate of eCommerce non-cooperators sales sE_(hp) 112, as describedin further detail below in connection with FIGS. 2 and 3 . In thisexample, the sales modeler 114 implements means for forming salesestimate. The means for forming sales estimate may additionally oralternatively be implemented by the processor 802 of FIG. 8 .

While the panel sales data 104 is unbiased with respect to source ofpurchase, there may be an unintended statistical bias in a panel withrespect to a much larger set of purchasers. To reduce (e.g., remove,reduce, etc.) any bias(es) present in the estimated eCommercenon-cooperators sales sE_(hp) 112, the example system 100 includes anexample bias reducer 122. The example bias reducer 122 of FIG. 1determines weights w_(hc) that represent discrepancies (e.g.,differences, ratios, etc.) between unbiased proportions ubCP_(hc) andpotentially biased proportions pcp_(hc). The unbiased proportionsubCP_(hc) are proportions of a large set of entities c (e.g., personshouseholds, etc.) based on different strata h. The unbiased proportionsubCP_(hc) may be obtained from a large-scale survey, such as areadership survey, a census, etc. The potentially biased proportionspcp_(hc) are proportions of a panel based on different strata h. Thesales modeler 114 estimates unbiased eCommerce non-cooperators salesubsE_(hp) 124 for a strata h and a product p based on the expansionfactor xF_(h) 116, the weights w_(hc), and the panel sales s_(hcp) 120for eCommerce non-cooperators recorded for the strata h and the productp, as described in further detail below in connection with FIGS. 4 and 5. In this example, the bias reducer 122 implements means for reducingbias. The means for reducing bias may additionally or alternatively beimplemented by the processor 802 of FIG. 8 .

To use commerce metrics to infer (e.g., model, project, estimate,ascertain, etc.) sales data for eCommerce non-cooperators, the examplesystem 100 includes an example data miner 126. Commerce metricsrepresent supplemental information related to aspects of the sales ofproducts. Example commerce metric include publicly available productinformation (e.g., consumer ratings, consumer comments, consumerquestions, etc.) from eCommerce non-cooperators websites. The exampledata miner 126 of FIG. 1 obtains the publicly available productinformation (e.g., ratings, feedback instances, comments, etc.) fromeCommerce non-cooperators websites using web scraping (e.g., webharvesting, web data extraction, etc.). The data miner 126 computes acontribution probability index (CPI) for each product sold by aneCommerce entity on its website, and uses the CPI to identify andcorrect for absent panel sales 128 for eCommerce non-cooperators thatwere not captured in the panel sales data 104. The data miner 126 alsouses the CPI and the estimated eCommerce non-cooperators sales sE_(hp)112 and/or the estimated unbiased eCommerce non-cooperators salesubsE_(hp) 124 to identify statistical panel gaps 130 (e.g., consumerclass, strata, category, etc.) in the composition of a panel that arestatistically underrepresented in the panel associated with the panelsales data 104, as described in further detail below in connection withFIGS. 6 and 7 . In this example, the data miner 126 implements means formining data. The means for mining data may additionally or alternativelybe implemented by the processor 802 of FIG. 8 .

While an example manner of implementing the system 100 is illustrated inFIG. 1 , one or more of the elements, processes and/or devicesillustrated in FIG. 1 may be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, the exampledata collector 106, the example sales modeler 114, the example biasreducer 122, the example data miner 126 and/or, more generally, theexample system 100 of FIG. 1 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example data collector 106, the examplesales modeler 114, the example bias reducer 122, the example data miner126 and/or, more generally, the example system 100 could be implementedby one or more analog or digital circuit(s), logic circuits,programmable processor(s), programmable controller(s), graphicsprocessing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)),application specific integrated circuit(s) (ASICNT(s)), programmablelogic device(s) (PLD(s)), field programmable gate array(s) (FPGA(s)),and/or field programmable logic device(s) (FPLD(s)). When reading any ofthe apparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example datacollector 106, the example sales modeler 114, the example bias reducer122, the example data miner 126, and/or the example system 100 is/arehereby expressly defined to include a non-transitory computer-readablestorage device or storage disk such as a memory, a digital versatiledisk (DVD), a compact disc (CD), a compact disc read-only memory(CD-ROM), a Blu-ray disk, etc. including the software and/or firmware.Further still, the example system 100 of FIG. 1 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 1 , and/or may include more than one of any or allof the illustrated elements, processes and devices. As used herein, thephrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

FIG. 2 is a block diagram illustrating an example implementation of thesales modeler 114 of FIG. 1 . To split sales data, the example salesmodeler 114 of FIG. 2 includes an example splitter 202. The examplesplitter 202 of FIG. 2 splits the panel sales data 104 into panel salesdata G* 204 for eCommerce cooperators, and panel sales data s 206 foreCommerce non-cooperators. In this example, the splitter 202 implementssecond means for splitting. The means for splitting may additionally oralternatively be implemented by the processor 802 of FIG. 8 .

To match sales data, the example sales modeler 114 of FIG. 2 includes anexample matcher 208. The example matcher 208 of FIG. 2 matches (e.g.,sorts, orders, rearranges, etc.) purchases of the eCommerce cooperatorssales data G* 102 based on product category (e.g., beauty, furniture,grocery, etc.) and/or by product to form matched eCommerce cooperatorssales data G 210. The example matcher 208 likewise matches (e.g., sorts,orders, etc.) sales in the panel sales data G* 204 for eCommercecooperators based on product category (e.g., beauty, furniture, grocery,etc.) and/or by product to form matched panel sales data G 212 foreCommerce cooperators. In this example, the matcher 208 implements meansfor matching. The means for matchng may additionally or alternatively beimplemented by the processor 802 of FIG. 8 .

To stratify sales data, the example sales modeler 114 includes anexample stratifier 214. The example stratifier 214 of FIG. 2 splits thematched eCommerce cooperators sales data G 210 based on strata h to formstratified eCommerce cooperators sales data G_(h) 118. Example strata hinclude, for example, city, region, socio-economic class, etc. Thestratifier likewise splits the panel sales data G 212 for eCommercecooperators into stratified panel sales data g_(h) 216 for eCommercecooperators, and splits the panel sales data s 206 for eCommercenon-cooperators into stratified panel sales data s_(h) 218 for eCommercenon-cooperators. In this example, the stratifier 214 implements firstmeans for stratifying. The first means for stratifying may additionallyor alternatively be implemented by the processor 802 of FIG. 8 .

To determine an expansion factor, the example sales modeler 114 includesan example factor determiner 220. The example factor determiner 220 ofFIG. 2 determines the expansion factor xF_(h) 116 that represents aratio of the amount of eCommerce cooperators sales data G_(h) 118 andpanels sales data g_(h) 216 for eCommerce cooperators for a strata h.The expansion factor xF_(h) 116 can be computed by the example factordeterminer 220 in a manner consistent with example mathematicalexpression of EQN (2A).

xF_(h) = CNT(G_(h))/CNT(g_(h))

CNT(x) is a count of the number of entries in x. For example, g_(h)contains an entry for each product purchase in the strata h and, thus,CNT(g_(h)) is the number of products purchased in the strata h. In thisexample, the factor determiner 220 implements means for determiningfactors. The means for determining factors may additionally oralternatively be implemented by the processor 802 of FIG. 8 .

To estimate eCommerce non-cooperators sales sE_(hp) 112, the examplesales modeler 114 includes an example non-cooperators estimator 222. Insome examples, the example non-cooperators estimator 222 of FIG. 2performs quality checks and/or outlier management for each product p andstrata h. For example, non-cooperators estimator 222 checks for andcorrects the panel sales data s_(h) 218 for eCommerce non-cooperatorsfor statistical consistency, trendability, and statistical aberrationsusing a statistical outlier correction process. The non-cooperatorsestimator 222 uses the expansion factor xF_(h) 116 to estimate eCommercenon-cooperators sales sE_(hp) 112 for a strata h and a product p using,for example, the example mathematical expression of EQN (2B).

sE_(hp) = xF_(h) * CNT(s_(hp))

In this example, the non-cooperators estimator 222 implements means forestimating non-cooperators sales. The means for estimatingnon-cooperators sales may additionally or alternatively be implementedby the processor 802 of FIG. 8 .

To compute estimated total eCommerce sales UE_(hp) 224, the examplesales modeler 114 of FIG. 2 includes an example sales estimator 226. Theexample sales estimator 226 of FIG. 2 combines the estimated eCommercenon-cooperators sales sE_(hp) 112 and the eCommerce cooperators salesdata G* 102 to estimate the total eCommerce sales UE_(hp) 224. Forexample, the sales estimator 226 combines the estimated eCommercenon-cooperators sales sE_(hp) 112 and the eCommerce cooperators salesdata G*_(hp) 102 for each strata h and productp using the examplemathematical expression of EQN (2C).

UE_(hp) = CNT(G*_(hp)) + sE_(hp)

In this example, the sales estimator 226 implements means for estimatingtotal sales. The means for estimating total sales may additionally oralternatively be implemented by the processor 802 of FIG. 8 .

To compute per-product sales and total eCommerce sales, the examplesales modeler 114 of FIG. 2 includes an example eCommerce estimator 228.The example eCommerce estimator 228 of FIG. 2 combines the estimatedtotal eCommerce sales UE_(hp) 224 for multiple strata h and a product pto obtain estimated sales UE_(p) 230 for the product p. For example, theestimated sales UE_(p) 230 can be computed by the example eCommerceestimator 228 using the example mathematical expression of EQN (2D)

UE_(p) = ∑UE_(hp)

In this example, the eCommerce estimator 228 implements means forestimating per-product sales. The means for estimating per-product salesmay additionally or alternatively be implemented by the processor 802 ofFIG. 8 .

The eCommerce estimator 228 combines the estimated sales UE_(p) 230 formultiple products to obtain an estimate of total eCommerce sales UE 232.The estimated total eCommerce sales UE 232 can be determined by theexample eCommerce estimator 228 using the example mathematicalexpression of EQN (2E).

UE = ∑UE_(p)

While an example manner of implementing the example sales modeler 114 ofFIG. 1 is illustrated in FIG. 2 , one or more of the elements, processesand/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example splitter 202, the example matcher 208, the examplestratifier 214, the example factor determiner 220, the eCommercenon-cooperators estimator 222, the example sales estimator 226, theexample eCommerce estimator 228 and/or, more generally, the examplesales modeler 114 of FIG. 2 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example splitter 202, the example matcher208, the example stratifier 214, the example factor determiner 220, theeCommerce non-cooperators estimator 222, the example sales estimator226, the example eCommerce estimator 228 and/or, more generally, theexample sales modeler 114 could be implemented by one or more analog ordigital circuit(s), logic circuits, programmable processor(s),programmable controller(s), GPU(s), DSP(s), ASICNT(s), PLD(s), FPGA(s),and/or FPLD(s). When reading any of the apparatus or system claims ofthis patent to cover a purely software and/or firmware implementation,at least one of the example splitter 202, the example matcher 208, theexample stratifier 214, the example factor determiner 220, the eCommercenon-cooperators estimator 222, the example sales estimator 226, theexample eCommerce estimator 228, and/or the example sales modeler 114is/are hereby expressly defined to include a non-transitorycomputer-readable storage device or storage disk such as a memory, aDVD, a CD, a CD-ROM, a Blu-ray disk, etc. including the software and/orfirmware. Further still, the example sales modeler 114 of FIG. 2 mayinclude one or more elements, processes and/or devices in addition to,or instead of, those illustrated in FIG. 2 , and/or may include morethan one of any or all of the illustrated elements, processes anddevices.

A flowchart representative of example hardware logic, machine-readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the sales modeler of FIG. 1 and/orFIG. 2 is shown in FIG. 3 . The machine-readable instructions may be anexecutable program or portion of an executable program for execution bya computer processor such as the processor 802 shown in the exampleprocessor platform 800 discussed below in connection with FIG. 8 . Theprogram may be embodied in software stored on a non-transitorycomputer-readable storage medium such as a CD, a CD-ROM, a floppy disk,a hard drive, a DVD, a Blu-ray disk, or a memory associated with theprocessor 802, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 802and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 3 , many other methods of implementing the example sales modeler114 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally, and/or alternatively,any or all of the blocks may be implemented by one or more hardwarecircuits (e.g., discrete and/or integrated analog and/or digitalcircuitry, an FPGA, an ASIC, a PLD, an FPLD, a comparator, anoperational-amplifier (op-amp), a logic circuit, etc.) structured toperform the corresponding operation without executing software orfirmware.

The program 300 of FIG. 3 begins at block 302 where, for example, thesplitter 202 splits the panel sales data 104 (panel purchases) intopanel sales data G* 204 for eCommerce cooperators, and panel sales datas 206 for eCommerce non-cooperators (block 302).

The example matcher 208 matches (e.g., sorts, orders, etc.) sales in thepanel sales data G* 204 for eCommerce cooperators based on productcategory (e.g., beauty, furniture, grocery, etc.) and/or by product toform matched panel sales data G 212 for eCommerce cooperators (block304). The matcher 208 likewise matches (e.g., sorts, orders, rearranges,etc.) purchases of the eCommerce cooperators sales data G* 102 based onproduct category (e.g., beauty, furniture, grocery, etc.) and/or byproduct to form matched eCommerce cooperators sales data G 210 (block306).

The stratifier 214 splits the matched eCommerce cooperators sales data G210 into strata h to form stratified eCommerce cooperators sales dataG_(h) 118, stratifies the panel sales data G 212 for eCommercecooperators into stratified panel sales data g_(h) 216 for eCommercecooperators, and splits the panel sales data s 206 for eCommercenon-cooperators into stratified panel sales data s_(h) 218 for eCommercenon-cooperators (block 308).

The factor determiner 220 computes an expansion factor xF_(h) 116 thatrepresents a ratio of the number of eCommerce cooperators sales dataG_(h) 118 and the number of panel sales data g_(h) 216 for eCommercecooperators for a strata h (block 310). The expansion factor xF_(h) 116can be computed by factor determiner 220 using the mathematicalexpression of EQN (2A).

In some examples, quality checks and/or outlier management for eachproduct p and strata h are carried out (block 312). The non-cooperatorsestimator 222 uses the expansion factor xF_(h) 116 to estimate eCommercenon-cooperators sales sE_(hp) 112 for a strata h and a product p using,for example, the example mathematical expression of EQN (2B) (block314).

The example sales estimator 226 combines the estimated eCommercenon-cooperators sales sE_(hp) 112 and the eCommerce cooperators salesdata G* 102 to estimate the total eCommerce sales UE_(hp) 224 (block316). For example, the sales estimator 226 combines the estimatedeCommerce non-cooperators sales sE_(hp) 112 and the eCommercecooperators sales data G*_(hp) 102 for each strata h and product p usingthe example mathematical expression of EQN (2C).

The example eCommerce estimator 228 combines the total eCommerce salesUE_(hp) 224 for multiple strata h to obtains estimated per-product salesUE_(p) 230 (block 318). The eCommerce estimator 228 combines theestimated per-product sales UE_(p) 230 for multiple products to obtainan estimate of total eCommerce sales UE 232 (block 318). Control thenexits from the example program 300 of FIG. 3 .

FIG. 4 is a block diagram illustrating an example implementation of theexample bias reducer 122 of FIG. 1 . Panels are typically limited insize due to, for example, practical concerns (e.g., the willingness ofpersons to participate in a panel, costs, etc.). Such size constraintscan result in statistical biases in the composition of the panel and,thus, statistical biases in sales data recorded by the panel.Traditional discretionary behaviors, guesswork, and/or reliance onheuristics can produce further erroneous and/or otherwise biasedresults. The example bias reducer 122 of FIG. 4 computes unbiased salesestimates in a manner devoid of analyst discretion, guesswork and/orbias. Accordingly, more accurate sales estimates can be obtained frombiased panel sales data g* 206 and s 206. Therefore, the examplemethods, systems, apparatus and articles of manufacture disclosed hereinto model eCommerce sales are more computationally efficient thantraditional methods by accurately providing unbiased sales estimatesthat reflect a full range of products and strata, without having torecruit, manage and pay for large numbers of panel members.

To stratify a set of profiles c 402 of a large set of purchasers, theexample bias reducer 122 of FIG. 4 includes an example stratifier 404.The example stratifier 404 of FIG. 4 splits the set of profiles c 402(e.g., a profile universe) of the large set of purchasers intostratified unbiased consumer profiles ubCP_(hc) 406. The stratifiedunbiased consumer profiles ubCP_(hc) 406 reflects the numbers ofpurchaser in a plurality of strata h. A stratified set of panel profilespcp_(hc) 408 of the members of a panel reflects the numbers of panelmembers in each of the plurality of strata h. Differences between theunbiased consumer profiles ubCP_(hc) 406 and the stratified set of panelprofiles pcp_(hc) 408 may represent a bias in panel membership. In thisexample, the stratifier 404 implements first means for stratifying. Themeans for stratifying may additionally or alternatively be implementedby the processor 802 of FIG. 8 .

The set of profiles c 402 can be obtained using large-scale surveys,such as, readership surveys, a census, etc. that are intended torepresent the full population of consumers in terms of attributes, suchas, age, gender, location, socio-economic class, household size andcomposition, etc. These attributes can be used to stratify the set ofprofiles c 402 and the panel members.

To reduce bias in panel sales data, the example bias reducer 122 of FIG.4 includes an example weight determiner 410. The example weightdeterminer 410 of FIG. 4 calculates weights w_(hc) 412 that reflectdifferences (e.g., ratios, etc.) between the unbiased consumer profilesubCP_(hc) 406 and the stratified set of panel profiles pcp_(hc) 408. Anexample mathematical expression that can be used by the example weightdeterminer 410 to compute the weights w_(hc) 412 is shown in EQN (4A).

w_(hc) = (ubCP_(hc) ÷ ∑ubCP_(hc))/(pcp_(hc) ÷ ∑pcp_(hc))

In this example, the weight determiner 410 implements means fordetermining weights. The means for determining weights may additionallyor alternatively be implemented by the processor 802 of FIG. 8 .

To estimate unbiased eCommerce non-cooperators sales ubsE_(hp) 414 forthe set of profiles c, the example bias reducer 122 of FIG. 4 includesan example unbiased non-cooperators estimator 416. The unbiasednon-cooperators estimator 416 uses the expansion factor xF_(h) 116 andthe weights w_(hc) 412 to estimate unbiased eCommerce non-cooperatorssales ubsE_(hcp) for one of the set of profiles c, a strata h and aproduct p. For example, the unbiased product estimator 420 can estimatethe unbiased eCommerce non-cooperators sales ubsE_(hcp) using, forexample, the example mathematical expression of EQN (4B).

ubsE_(hcp) = xF_(h) ^(*)w_(hc) ^(*)CNT(s_(hcp))

The unbiased non-cooperators estimator 416 combines the unbiasedeCommerce non-cooperators sales ubsE_(hcp) to estimate the eCommercenon-cooperators sales ubsE_(hp) 414 for a strata h and a product p. Insome examples, the unbiased non-cooperators estimator 416 sums theunbiased eCommerce non-cooperators sales ubsE_(hcp), as shown in theexample mathematical expression of EQN (4C).

ubsE_(hp) = ∑ubsE_(hcp)

In this example, the unbiased non-cooperators estimate 416 implementsmeans for estimating unbiased non-cooperators sales. The means forestimating unbiased non-cooperators sales may additionally oralternatively be implemented by the processor 802 of FIG. 8 .

To calculate estimated unbiased sales ubUE_(hp) 418 for each product pand strata h, the example bias reducer 122 of FIG. 4 includes an exampleunbiased product estimator 420. The example unbiased product estimator420 of FIG. 4 combines the estimated unbiased eCommerce non-cooperatorssales ubsE_(hcp) with the eCommerce cooperators sales data G* 102 toform the estimated unbiased sales ubUE_(hp) 418 for each product p andstrata h. The example unbiased product estimator 420 of FIG. 4 cancombine the estimated unbiased eCommerce non-cooperators salesubsE_(hcp) with the eCommerce cooperators sales data G* 102 using theexample mathematical expression of EQN (4D).

ubUE_(hp) = G_(hp)^(*) + ubsE_(hp)

In this example, the unbiased product estimator 420 implements means forestimating unbiased sales. The means for estimating unbiased productsales may additionally or alternatively be implemented by the processor802 of FIG. 8 .

To estimate unbiased per-product sales ubUE_(p) 422 and overall salesestimate ubUE 424, the example bias reducer 122 includes an exampleunbiased total estimator 426. The unbiased total estimator 426 combinesthe estimated unbiased sales ubUE_(hp) 418 across the strata h to formthe per-product sales ubUE_(p) 422. The total estimator 426 can form theproduct sales ubUE_(p) 422 using the example mathematical expression ofEQN (4E).

ubUEp = ∑ubUE_(hp)

The unbiased total estimator 426 combines the unbiased product salesubUEp 422 across the products p to form the overall sales estimate ubUE424. The total estimator 426 can form the overall sales estimate ubUE424 using the example mathematical expression of EQN (4F).

ubUE = ∑ubUE_(p)

In this example, the unbiased total estimator 222 implements means forestimating unbiased total sales. The means for estimating unbiased totalsales may additionally or alternatively be implemented by the processor802 of FIG. 8 .

While an example manner of implementing the bias reducer 122 of FIG. 1is illustrated in FIG. 4 , one or more of the elements, processes and/ordevices illustrated in FIG. 4 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample stratifier 404, the example weight determiner 410, the exampleunbiased non-cooperators estimator 416, the example unbiased productestimator 420, the example unbiased total estimator 426, and/or, moregenerally, the example bias reducer 122 of FIG. 4 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the examplestratifier 404, the example weight determiner 410, the example unbiasednon-cooperators estimator 416, the example unbiased product estimator420, the example unbiased total estimator 426, and/or, more generally,the example bias reducer 122 of FIG. 4 could be implemented by one ormore analog or digital circuit(s), logic circuits, programmableprocessor(s), programmable controller(s), GPU(s), DSP(s), ASICNT(s),PLD(s), FPGA(s), and/or FPLD(s). When reading any of the apparatus orsystem claims of this patent to cover a purely software and/or firmwareimplementation, at least one of the example stratifier 404, the exampleweight determiner 410, the example unbiased non-cooperators estimator416, the example unbiased product estimator 420, the example unbiasedtotal estimator 426, and/or the example bias reducer 122 is/are herebyexpressly defined to include a non-transitory computer-readable storagedevice or storage disk such as a memory, a DVD, a CD, a CD-ROM, aBlu-ray disk, etc. including the software and/or firmware. Furtherstill, the example bias reducer 122 of FIG. 4 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 4 , and/or may include more than one of any or allof the illustrated elements, processes and devices.

A flowchart representative of example hardware logic, machine-readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the bias reducer 122 of FIG. 1and/or FIG. 4 is shown in FIG. 5 . The machine-readable instructions maybe an executable program or portion of an executable program forexecution by a computer processor such as the processor 802 shown in theexample processor platform 800 discussed below in connection with FIG. 8. The program may be embodied in software stored on a non-transitorycomputer-readable storage medium such as a CD, a CD-ROM, a floppy disk,a hard drive, a DVD, a Blu-ray disk, or a memory associated with theprocessor 802, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 802and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 5 , many other methods of implementing the example bias reducer122 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally, and/or alternatively,any or all of the blocks may be implemented by one or more hardwarecircuits (e.g., discrete and/or integrated analog and/or digitalcircuitry, an FPGA, an ASIC, a PLD, an FPLD, a comparator, anoperational-amplifier (op-amp), a logic circuit, etc.) structured toperform the corresponding operation without executing software orfirmware.

The program 500 of FIG. 5 begins at block 502 where, for example, thestratifier 404 splits the set of profiles c 402 of the large set ofpurchasers into stratified unbiased consumer profiles ubCP_(hc) 406(block 502).

The weight determiner 410 of FIG. 4 calculates weights w_(hc) 412 thatreflect differences (e.g., ratios, etc.) between the unbiased consumerprofiles ubCP_(hc) 406 and the stratified set of panel profiles pcp_(hc)408 (block 504) using, for example, the example mathematical expressionof EQN (4A).

The unbiased product estimator 420 uses the expansion factor xF_(h) 116and the weights w_(hc) 412 to estimate unbiased eCommercenon-cooperators sales ubsE_(hcp) for one of the set of profiles c, astrata h and a product p (block 506) using, for example, the examplemathematical expression of EQN (4B), and combines the unbiased eCommercenon-cooperators sales ubsE_(hcp) to estimate the eCommercenon-cooperators sales ubsE_(hp) 414 for a strata h and a product pusing, for example, the example mathematical expression of EQN (4C). Theexample unbiased product estimator 420 of FIG. 4 combines the estimatedunbiased eCommerce non-cooperators sales ubsE_(hcp) with the eCommercecooperators sales data G* 102 to form the estimated unbiased salesubUE_(hp) 418 for each product p and strata h (block 508) using, forexample, the example mathematical expression of EQN (4D).

The unbiased total estimator 426 combines the estimated unbiased salesubUE_(hp) 418 across the strata h to form the per-product sales ubUE_(p)422 (block 510) using, for example, the example mathematical expressionof EQN (4E), and combines the unbiased per-product sales ubUE_(p) 422across the products p to form the overall sales estimate ubUE 424 using,for example, the example mathematical expression of EQN (4F). Controlthen exits from the example program 500 of FIG. 5

Panel sizes can result in an under-representation (e.g., partially,wholly, etc.) of purchases from eCommerce cooperators and/or eCommercenon-cooperators. Accordingly, eCommerce sales estimates can fail tocapture all aspects of eCommerce sales. Therefore, marketing, sales,etc. decisions by manufacturers, eCommerce entities, etc. based onrecorded panel purchases can be incorrect, requiring traditionaldiscretionary behaviors, guesswork, and/or reliance on heuristics toreduce. In contrast, the example data miner 126 captures commerce metricdata representing a large set of eCommerce entities, and uses thecommerce metric data to extrapolate from sales recorded by the panel toform sales estimates for a much larger set of eCommerce entities forwhich recorded panel sales data is not available.

FIG. 6 is a block diagram illustrating an example implementation of theexample data miner 126 of FIG. 1 . The example data miner 126 of FIG. 6processes commerce metrics 602 (e.g., publicly available productinformation, such as ratings R_(p) 604, feedback instances F_(p) 606,comments and/or questions C_(p) 608, etc.). The ratings R_(p) 604,feedback instances F_(p) 606, comments and/or questions C_(p) 608 isobtained, for example, from eCommerce non-cooperators websites using webscraping (e.g., web harvesting, web data extraction, etc.).

To determine traffic counts T_(p) 610 for products p, the example dataminer 126 of FIG. 6 includes an example traffic estimator 612. Theexample traffic estimator 612 of FIG. 6 combines the scraped feedbackinstances F_(p) 606 and the number of comments and/or questions C_(p)608 to estimate the number views of a product p on eCommercenon-cooperators websites. In some examples, the traffic estimator 612computes the traffic counts T_(p) 610 using the example mathematicalexpression of EQN (6A).

T_(p) = F_(p) + CNT(C_(p))

In this example, the traffic estimator 612 implements means forestimating traffic counts. The means for estimating traffic counts mayadditionally or alternatively be implemented by the processor 802 ofFIG. 8 .

To characterize sentiments S_(p) 614 expressed in the comments and/orquestions C_(p) 608, the example data miner 126 of FIG. 6 includes anexample natural language processor 616. The example natural languageprocessor 616 of FIG. 6 processes the scraped comments and/or questionsC_(p) 608 to assess the consumer, shopper, etc. sentiments S_(p) 614(e.g., attitude, feeling, mood, emotion, etc.) of viewers of a product pas conveyed by their comments and/or questions C_(p) 608. In someexamples, a sentiment S_(p) 614 is assigned a value selected from {-1 =completely negative, 0 = neutral, +1 = completely positive, between -1and 0 = negative, between 0 and 1 = positive}. Natural languageprocessing involves converting blocks of text into formalrepresentations that can be used to manipulate and determine, amongother things, the sentiment or emotion of the writer. Generallyspeaking, sentiment analysis aims to determine the attitude of thewriter with respect to the online product they are commenting on. Inthis example, the example natural language processor 616 implementsmeans for processing language. The means for processing language mayadditionally or alternatively be implemented by the processor 802 ofFIG. 8 .

To calculate commerce metric difference values, the example data miner126 of FIG. 6 includes an example trend identifier 618. In someexamples, commerce metric difference values are temporal changes of theratings R_(p) 604, the traffic counts T_(p) 610 and/or the sentimentsS_(p) 614. The example trend identifier 618 of FIG. 6 computes a trendmetric (e.g., a change) ΔT_(p) 620 in the traffic counts T_(p) 610 usingthe example mathematical expression of EQN (6B).

ΔT_(p) = T_(p)[current] − T_(p)[previous]

where current refers to a first time period (e.g., a current timeperiod), and previous refers to a second time period (e.g., a previoustime period). In examples disclosed herein, two time periods are used tocalculate the values of each input during the time period in which thesales happened. For example, if a previous time period had an averagerating of 3.0 given by 100 people, and the current time period had anaverage rating was 3.1 given by 110 people, 10 additional peopleprovided ratings in the current period, and these 10 people would havegiven an average rating of [(3.1 × 110) - (3.0 × 100)] = (110 - 100) =4.1. So we need to calculate this ΔR_(p) as this is the influencer tosales in this period. In some examples, the trend identifier 618computes a trend metric (e.g., a change) ΔR_(p) 622 in the ratings R_(p)604 using the example mathematical expression of EQN (6C).

$\Delta R_{p} = \left\lbrack \begin{array}{l}{R_{p}\left\lbrack {current} \right\rbrack^{\ast}T_{p}\left\lbrack {current} \right\rbrack -} \\{R_{p}\left\lbrack {previous} \right\rbrack^{\ast}T_{p}\left\lbrack {previous} \right\rbrack}\end{array} \right\rbrack/\Delta T_{p}$

In some examples, the trend identifier 618 computes a trend metric(e.g., a change) ΔS_(p) 624 in the sentiments Sp 614 using the examplemathematical expression of EQN (6D).

$\Delta S_{p} = \left\lbrack \begin{array}{l}{S_{p}\left\lbrack {current} \right\rbrack^{\ast}T_{p}\left\lbrack {current} \right\rbrack -} \\{S_{p}\left\lbrack {previous} \right\rbrack^{\ast}T_{p}\left\lbrack {previous} \right\rbrack}\end{array} \right\rbrack/\Delta T_{p}$

In this example, the trend identifier 618 implements means fordetermining trends. The means for determining trends may additionally oralternatively be implemented by the processor 802 of FIG. 8 .

To split trend data, the example data miner 126 includes an examplesplitter 626. The example splitter 626 of FIG. 6 splits (e.g.,segregates, etc.) the trend data ΔT_(p) 620, ΔR_(p) 622, and ΔS_(p) 624according different sets of eCommerce entities. For example, thesplitter 626 splits the trend data ΔT_(p) 620, ΔR_(p) 622, and ΔS_(p)624 into a non-cooperator set 628 associated with a first dataset ofnon-cooperators eCommerce entities, and a cooperator set 630 associatedwith a second dataset of cooperators eCommerce entities. In thisexample, the splitter 626 implements second means for splitting. Thesecond means for splitting may additionally or alternatively beimplemented by the processor 802 of FIG. 8 .

To predict sales, the example data miner 126 of FIG. 6 includes anexample machine learning engine 632. The example machine learning engine632 of FIG. 6 estimates eCommerce non-cooperators sales M_(p) 634 basedtrend data sets 628, 630. The machine learning engine 632 is trainedwith supervised learning with the cooperator set 630 of the trend dataΔT_(p) 620, ΔR_(p) 622, and ΔS_(p) 624 providing inputs for the machinelearning engine 632. Outputs of the machine learning engine 632 arecompared with the eCommerce cooperators sales data G_(p)* 102 and usedto update coefficients of the machine learning engine 632. A firstportion of the cooperator set 630 of the trend data ΔT_(p) 620, ΔR_(p)622, and ΔS_(p) 624 can be used to train the machine learning engine632, and a second portion of the cooperator set 630 of the trend dataΔT_(p) 620, ΔR_(p) 622, and ΔS_(p) 624 can be used to test the machinelearning engine 632. After the machine learning engine 632 is trained,the non-cooperator set 628 of the trend data ΔT_(p) 620, ΔR_(p) 622, andΔS_(p) 624 (training data) is passed through the machine learning engine632 to form the estimated eCommerce non-cooperators sales M_(p) 634. Insome examples, the machine learning engine 632 uses supervised machinelearning techniques. The machine learning engine 632 iterates throughthe training data and determines coefficients for which the machinelearning engine 632 most closely estimates the eCommerce cooperatorssales data G_(p)* 102 (a.k.a. truth data, target data, etc.) for a givenperiod using the web-scraped ratings R_(p) 604, traffic counts T_(p) 610and/or sentiments S_(p) 614 for the same product p for the same period.In this example, the machine learning engine 632 implements means forpredicting. The means for predicting may additionally or alternativelybe implemented by the processor 802 of FIG. 8 .

To determine per-product CPIs 636, the example data miner 126 of FIG. 6includes an example CPI determiner 638. The example CPI determiner 638of FIG. 6 computes a CPI_(p_co) for each product p sold by an eCommercecooperator entity on its website. An example CPI_(p) value representswhat percentage of all products sold are product p. The CPI_(p_co)values are the relative value of G_(p)* 102 for a product divided by asum of the outputs M_(p) 634 of the machine learning engine 632 and theeCommerce cooperators sales data G_(p)*. The CPI_(p_co) for each productp can be computed by the example CPI determiner 638 using the examplemathematical expression of EQN (6E).

CPI_(p_co) = G_(p) * /∑(G_(p) * +M_(p))

The CPI determiner 638 likewise computes a CPI_(p_nc) value for eachproduct p sold by an eCommerce non-cooperator on its website. TheCPI_(p_nc) values are the relative value of M_(p) 634 divided by the sumthe outputs M_(p) of the machine learning engine 632 and the eCommercecooperators sales data G_(p)*. The CPI_(p_nc) for each product p can becomputed by the CPI determiner 638 using the example mathematicalexpression of EQN (6F).

CPI_(p_nc) = M_(p)/∑(G_(p) * +M_(p))

In this example, the CPI determiner 638 implements means for determiningproduct ratios. The means for determining product ratios mayadditionally or alternatively be implemented by the processor 802 ofFIG. 8 .

To correct for absent product purchases, the example data miner 126 ofFIG. 6 includes an example sales allocator 640. In some examples,missing products are products with a higher CPI (indicating they arelikely to have been purchased) but are not present in the panel salesdata 104. These products are expected to have been sold by an eCommerceentity but, for some reason, are absent from the panel sales data 104.The sales allocator 640 divides a list of products identified from theavailable product information (e.g., ratings R_(p) 604, feedbackinstances F_(p) 606, comments and/or questions C_(p) 608, etc.) into aset of absent products {set-a} and a set of present products {set-p}.The sales allocator 640 reallocates sales of {set-p} into allocatedsales sa_(p) for {set-a} 640, and absent panel sales sa_(p) for {set-p}128 for eCommerce non-cooperators. For example, the sales allocator 640reallocates sales according to the example mathematical expression ofEQN (6G).

sa_(p)^({set − a}) : sa_(p)^({set − p}) = sE_(p)^({set − p}) * [CPI_(p)^({set − a}) : CPI_(p)^({set − p})]

Where sE_(p) is the eCommerce non-cooperators estimates sE_(hp) 110 ofFIGS. 1 and 2 , or the unbiased eCommerce non-cooperators estimatesubsE_(hp) 414 of FIG. 4 . In the example of EQN (6G), the CPI_(p) valuesare used to maintain the relative amounts of products sold. EQN (6G)determines how many sales to allocate to products that do not haverecorded sales in the panel sales data 104. By allocating sales as shownin EQN (6D), overestimation of the sales of products present in thepanel sales data 104 can be reduced. In this example, the salesallocator 640 implements means for allocating sales. The means forallocating sales may additionally or alternatively be implemented by theprocessor 802 of FIG. 8 .

To identify statistical gaps in panel composition (e.g., a missingconsumer class, strata, etc.), the example data miner 126 of FIG. 6includes an example panel gap analyzer 642. The example panel gapanalyzer 642 of FIG. 6 uses the CPI values 636 and sales estimates forthe panel sE to compute panel gaps PG 130. For example, the panel gapanalyzer 642 can use the example mathematical expression of EQN (6H) tocompute panel gaps PG_(p) per product p.

PG_(p) = CPI_(p) − (sE_(p)/∑sE_(p)),

where sE_(p) is the eCommerce non-cooperators estimates sE_(hp) 110 ofFIGS. 1 and 2 , or the unbiased eCommerce non-cooperators estimatesubsE_(hp) 414 of FIG. 4 . In EQN (6H), sE_(p) is divided by its sum toform a relative value, thereby corresponding with the relative valuedCPI_(p) values 636. The panel gap analyzer 642 identifies missing aspectof a panel (e.g., a consumer class, a strata, etc.) by splitting thepanel gaps by product p, strata h, and consumer class c using, forexample, the example mathematical expression of EQN (6I).

PG_(hc) = ∑[PG_(p) * (sE_(hcp)/sE_(cp))]

The panel gaps PG_(hc) identifies which aspect of the panel isresponsible for the absent products, thereby assisting in therecruitment of new panel members. In this example, the panel gapanalyzer implements means for determining gaps. The means fordetermining gaps may additionally or alternatively be implemented by theprocessor 802 of FIG. 8 .

While an example manner of implementing the data miner 126 of FIG. 1 isillustrated in FIG. 6 , one or more of the elements, processes and/ordevices illustrated in FIG. 6 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample traffic estimator 612, the example natural language processor616, the example trend identifier 618, the example splitter 626, theexample machine learning engine 632, the example CPI determiner 638, theexample sales allocator 640, the example panel gap analyzer 642 and/or,more generally, the example data miner 126 of FIG. 6 may be implementedby hardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example trafficestimator 612, the example natural language processor 616, the exampletrend identifier 618, the example splitter 626, the example machinelearning engine 632, the example CPI determiner 638, the example salesallocator 640, the example panel gap analyzer 642 and/or, moregenerally, the example data miner 126 could be implemented by one ormore analog or digital circuit(s), logic circuits, programmableprocessor(s), programmable controller(s), GPU(s), DSP(s), ASICNT(s),PLD(s), FPGA(s), and/or FPLD(s). When reading any of the apparatus orsystem claims of this patent to cover a purely software and/or firmwareimplementation, at least one of the example traffic estimator 612, theexample natural language processor 616, the example trend identifier618, the example splitter 626, the example machine learning engine 632,the example CPI determiner 638, the example sales allocator 640, theexample panel gap analyzer 642 and/or the example data miner 126 is/arehereby expressly defined to include a non-transitory computer-readablestorage device or storage disk such as a memory, a DVD, a CD, a CD-ROM,a Blu-ray disk, etc. including the software and/or firmware. Furtherstill, the example data miner 126 of FIG. 6 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 6 , and/or may include more than one of any or allof the illustrated elements, processes and devices.

A flowchart representative of example hardware logic, machine-readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the data miner 126 of FIG. 1 and/orFIG. 6 is shown in FIG. 7 . The machine-readable instructions may be anexecutable program or portion of an executable program for execution bya computer processor such as the processor 802 shown in the exampleprocessor platform 800 discussed below in connection with FIG. 8 . Theprogram may be embodied in software stored on a non-transitorycomputer-readable storage medium such as a CD, a CD-ROM, a floppy disk,a hard drive, a DVD, a Blu-ray disk, or a memory associated with theprocessor 802, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 802and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchart illustratedin FIG. 7 , many other methods of implementing the example data miner126 may alternatively be used. For example, the order of execution ofthe blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally, and/or alternatively,any or all of the blocks may be implemented by one or more hardwarecircuits (e.g., discrete and/or integrated analog and/or digitalcircuitry, an FPGA, an ASIC, a PLD, an FPLD, a comparator, anoperational-amplifier (op-amp), a logic circuit, etc.) structured toperform the corresponding operation without executing software orfirmware.

The program 700 of FIG. 7 begins at block 702 where the data miner 126and/or the data collector 106 scrapes eCommerce websites to obtainratings R_(p) 604, feedback instances F_(p) 606, comments and/orquestions C_(p) 608, etc. (block 702). The example traffic estimator 612combines the scraped feedback instances F_(p) 606 and the number ofcomments and/or questions C_(p) 608 to estimate the number views of aproduct p on eCommerce non-cooperators websites (block 704) using, forexample, the example mathematical expression of EQN (6A).

The example natural language processor 616 of FIG. 6 processes thescraped comments and/or questions C_(p) 608 to assess the sentimentsS_(p) 614 (e.g., attitude, feeling, mood, emotion, etc.) of viewers of aproduct p as conveyed by their comments and/or questions C_(p) 608(block 706).

The example trend identifier 618 of FIG. 6 calculates eCommerce metricdifferences (e.g., temporal changes) ΔT_(p) 620, ΔR_(p) 622, and ΔS_(p)624 in the ratings R_(p) 604, the traffic counts T_(p) 610 and thesentiments S_(p) 614 (block 708). For example, the trend identifier 618computes the eCommerce metric differences (e.g., temporal changes)ΔT_(p) 620, ΔR_(p) 622, and ΔS_(p) 624 using the example mathematicalexpressions of EQN (6B), EQN (6C), and EQN (6D).

The example splitter 626 of FIG. 6 splits the trend data ΔT_(p) 620,ΔR_(p) 622, and ΔS_(p) 624 into a non-cooperator set 628 and acooperator set 630 (block 710).

The machine learning engine 632 is trained with supervised learning withthe cooperator set 630 of the trend data ΔT_(p) 620, ΔR_(p) 622, andΔS_(p) 624 as inputs of the machine learning engine 632, and outputs ofthe machine learning engine 632 compared with the eCommerce cooperatorssales data G_(p)* 102 to update coefficients of the machine learningengine 632 (block 712).

The non-cooperator set 628 of the trend data ΔT_(p) 620, ΔR_(p) 622, andΔS_(p) 624 is passed through the machine learning engine 632 to formestimates M_(p) 634 of eCommerce non-cooperators sales (block 714).

The example CPI determiner 638 of FIG. 6 computes CPI_(p_co) andCPl_(p_nc) values for each productp sold by an eCommerce cooperatorentity on its website (block 716) using, for example, the mathematicalexpressions of EQN (6E), EQN (6F) and EQN (6G).

The sales allocator 640 divides a list of products identified from theavailable product information (e.g., ratings R_(p) 604, feedbackinstances F_(p) 606, comments and/or questions C_(p) 608, etc.) into aset of absent products {set-a} and a set of present products {set-p},and reallocates sales of {set-p} into allocated sales sa_(p) for {set-a}640 and sa_(p) for {set-p} (block 718) using, for example, the examplemathematical expression of EQN (6G)

The example panel gap analyzer 642 of FIG. 6 uses the CPI values 636 andsales estimates for the panel sE to compute panel gaps PG 130 (block720) using, for example, the mathematical expressions of EQN (6H) andEQN (6I). Control then exits from the example program 700 of FIG. 7 .

As mentioned above, the example processes of FIGS. 3, 5 and 7 may beimplemented using executable instructions (e.g., computer and/ormachine-readable instructions) stored on a non-transitory computerand/or machine-readable medium such as a hard disk drive, a flashmemory, a read-only memory, a CD, a CD-ROM, a DVD, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer-readable medium is expressly defined to includeany type of computer-readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

FIG. 8 is a block diagram of an example processor platform 800structured to execute the instructions of FIGS. 3, 5 and 7 to implementthe example system 100 of FIG. 1 , the example data collector 106, theexample sales modeler 114 of FIG. 1 and FIG. 2 , the example biasreducer 122 of FIG. 1 and FIG. 4 , the example data miner 126 of FIG. 1and FIG. 6 . The processor platform 800 can be, for example, a server, apersonal computer, a workstation, or any other type of computing device.

The processor platform 800 of the illustrated example includes aprocessor 802. The processor 802 of the illustrated example is hardware.For example, the processor 802 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example data collector 106,the example sales modeler 114, the example bias reducer 122, the exampledata miner 126, the example splitter 202, the example matcher 208, theexample stratifier 214, the example factor determiner 220, the examplenon-cooperators estimator 222, the example sales estimator 226, theexample eCommerce estimator 228, the example stratifier 404, the exampleweight determiner 410, the example unbiased non-cooperators estimator416, the example unbiased product estimator 420, the example unbiasedtotal estimator 426, the example traffic estimator 612, the examplenatural language processor 616, the example trend identifier 618, theexample splitter 626, the example machine learning engine 632, theexample CPI determiner 638, the example sales allocator 640, and theexample panel gap analyzer 642.

The processor 802 of the illustrated example includes a local memory 804(e.g., a cache). The processor 802 of the illustrated example is incommunication with a main memory including a volatile memory 806 and anon-volatile memory 808 via a bus 810. The volatile memory 806 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 808 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 806, 808is controlled by a memory controller.

The processor platform 800 of the illustrated example also includes aninterface circuit 812. The interface circuit 812 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 814 are connectedto the interface circuit 812. The input device(s) 814 permit(s) a userto enter data and/or commands into the processor 802. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 816 are also connected to the interfacecircuit 812 of the illustrated example. The output devices 816 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 812 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 812 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 818. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 800 of the illustrated example also includes oneor more mass storage devices 820 for storing software and/or data.Examples of such mass storage devices 820 include floppy disk drives,hard drive disks, CD drives, Blu-ray disk drives, redundant array ofindependent disks (RAID) systems, and DVD drives.

Coded instructions 822 including the coded instructions of FIGS. 3, 5and 7 may be stored in the mass storage device 820, in the volatilememory 806, in the non-volatile memory 808, and/or on a removablenon-transitory computer-readable storage medium such as a CD-ROM or aDVD.

From the foregoing, it will be appreciated that example methods,systems, apparatus and articles of manufacture have been disclosed thatmodel eCommerce sales. From the foregoing, it will be appreciated thatmethods, systems, apparatus and articles of manufacture have beendisclosed that enhance the operations of a computer to improve theaccuracy and completeness of eCommerce sales estimates. The disclosedmethods, systems, apparatus and articles of manufacture improve theefficiency of using a computing device by enabling the use of easilyobtained publicly available commerce metrics to estimate eCommerce salesfor a much larger number of eCommerce entities. Thereby increasing thecompleteness and statistical accuracy of eCommerce sales measurement.Thus, the disclosed methods, apparatus and articles of manufacture areaccordingly directed to one or more improvement(s) in the functioning ofa computer.

Example methods, systems, apparatus and articles of manufacture to modelecommerce sales are disclosed herein. Further examples and combinationsthereof include at least the following.

Example 1 is a system to model eCommerce sales, the system including atrend identifier to compute commerce metric differences corresponding toproducts, the commerce metric differences based on first commercemetrics scraped at a first time and second commerce metrics scraped at asecond time, a splitter to split the commerce metric differences into afirst portion of the commerce metric differences corresponding to afirst dataset of eCommerce cooperators, and into a second portion of thecommerce metric differences corresponding to a second dataset ofeCommerce non-cooperators, a machine learning engine to infer sales databy estimating eCommerce non-cooperators sales based on the secondportion of the commerce metric differences, and a sales allocator toestimate sales missing from collected sales data based on the estimateeCommerce non-cooperators sales.

Example 2 is the system of example 1, wherein the machine learningengine is to train a model using the first portion of the commercemetric differences, and pass the second portion of the commerce metricdifferences through the model to estimate the eCommerce non-cooperatorssales.

Example 3 is the system of example 2, further including a contributionprobability index calculator to calculate product contributionprobability indices corresponding to the products for (a) the eCommercecooperators and (b) the eCommerce non-cooperators, the productcontribution probability indices based on the model.

Example 4 is the system of example 3, wherein the sales allocator is toestimate the sales missing from collected sales data based on theeCommerce non-cooperators and product contribution probability indices.

Example 5 is the system of example 1, further including a naturallanguage processor to form a first set of consumer sentiments from thefirst commerce metrics, and form a second set of consumer sentimentsfrom the second commerce metrics, wherein the commerce metricdifferences include differences between the first set of consumersentiments and the second set of consumer sentiments.

Example 6 is the system of example 1, wherein the first commerce metricsinclude first traffic metrics, and the second commerce metrics includesecond traffic metrics, wherein the commerce metric differences includedifferences between the first traffic metrics and the second trafficmetrics.

Example 7 is the system of example 6, wherein the first traffic metricsare based on a number of feedback instances and a number of comments.

Example 8 is the system of example 1, wherein the first commerce metricsinclude first ratings metrics, and the second commerce metrics includesecond ratings metrics, wherein the commerce metric differences includedifferences between the first ratings metrics and the second ratingsmetrics.

Example 9 is the system of example 1, further including a panel gapanalyzer to identify a statistical gap in a panel composition for aproduct by computing a difference of a first ratio of a first number ofsales of the product to a panel and a second number of sales of allproducts to the panel, and a second ratio of a third number of sales ofthe product recorded by eCommerce cooperators and a fourth number ofsales of all products to the eCommerce cooperators.

Example 10 is the system of example 9, wherein the statistical gap is afirst statistical gap in the panel composition for a first product, andthe panel gap analyzer is to identify a second statistical gap in thepanel composition for a second product, and combine the firststatistical gap and the second statistical gap to identify a thirdstatistical gap in the panel composition related to at least one of astrata, or a consumer class.

Example 11 is a system to model eCommerce sales, the system includingmeans for determining trends to compute commerce metric differencescorresponding to products, the commerce metric differences based onfirst commerce metrics scraped at a first time and second commercemetrics scraped at a second time, means for splitting to split thecommerce metric differences into a first portion of the commerce metricdifferences corresponding to a first dataset of eCommerce cooperators,and into a second portion of the commerce metric differencescorresponding to a second dataset of eCommerce non-cooperators, meansfor predicting to infer sales data by estimating eCommercenon-cooperators sales based on the second portion of the commerce metricdifferences, and means for allocating sales to allocate missing fromcollected sales data based on the estimate eCommerce non-cooperatorssales.

Example 12 is system of example 11, further including means fordetermining product ratios to calculate product contribution probabilityindices corresponding to the products for (a) the eCommerce cooperatorsand (b) the eCommerce non-cooperators, the product contributionprobability indices based on a sales prediction model.

Example 13 is system of example 12, wherein the allocating sales meansis to estimate the sales missing from collected sales data based on theeCommerce non-cooperators and the product contribution probabilityindices.

Example 14 is system of example 11, further including means for languageprocessing to form a first set of consumer sentiments from the firstcommerce metrics, and form a second set of consumer sentiments from thesecond commerce metrics, wherein the commerce metric differences includedifferences between the first set of consumer sentiments and the secondset of consumer sentiments.

Example 15 is system of example 11, wherein the first commerce metricsinclude first traffic metrics, the second commerce metrics includesecond traffic metrics, and the commerce metric differences includedifferences between the first traffic metrics and the second trafficmetrics.

Example 16 is system of example 15, wherein the first traffic metricsare based on a number of feedback instances and a number of comments.

Example 17 is system of example 11, wherein the first commerce metricsinclude first ratings metrics, and the second commerce metrics includesecond ratings metrics, wherein the commerce metric differences includedifferences between the first ratings metrics and the second ratingsmetrics.

Example 18 is system of example 11, further including means fordetermining gaps to identify a statistical gap in a panel compositionfor a product by computing a difference of a first ratio of a firstnumber of sales of the product to a panel and a second number of salesof all products to the panel, and a second ratio of a third number ofsales of the product recorded by eCommerce cooperators and a fourthnumber of sales of all products to the eCommerce cooperators.

Example 19 is system of example 18, wherein the statistical gap is afirst statistical gap in the panel composition for a first product, andthe determining gaps means is to identify a second statistical gap inthe panel composition for a second product, and combine the firststatistical gap and the second statistical gap to identify a thirdstatistical gap in the panel composition related to at least one of astrata, or a consumer class.

Example 20 is a non-transitory computer-readable storage mediumincluding instructions that, when executed, cause a machine to calculatecommerce metric differences corresponding to products, the commercemetric differences based on first commerce metrics scraped at a firsttime and second commerce metrics scraped at a second time, split thecommerce metric differences into a first portion of the commerce metricdifferences corresponding to a first dataset of eCommerce cooperators,and into a second portion of the commerce metric differencescorresponding to a second dataset of eCommerce non-cooperators, estimateeCommerce non-cooperators sales based on the second portion of thecommerce metric differences to infer sales data, and estimate salesmissing from collected sales data based on the estimate eCommercenon-cooperators sales.

Example 21 is the non-transitory computer-readable storage medium ofexample 20, wherein the instructions, when executed, cause the machineto estimate the eCommerce non-cooperators sales based on the secondportion of the commerce metric differences by training a model using thefirst portion of the commerce metric differences, and passing the secondportion of the commerce metric differences through the model to estimatethe eCommerce non-cooperators sales.

Example 22 is the non-transitory computer-readable storage medium ofexample 21, wherein the instructions, when executed, cause the machineto calculate product contribution probability indices corresponding tothe products for (a) the eCommerce cooperators and (b) the eCommercenon-cooperators, the product contribution probability indices based onthe model.

Example 23 is the non-transitory computer-readable storage medium ofexample 22, wherein the instructions, when executed, cause the machineto estimate the sales missing from collected sales data based on theeCommerce non-cooperators and the product contribution probabilityindices.

Example 24 is the non-transitory computer-readable storage medium ofexample 20, wherein the instructions, when executed, cause the machineto apply natural language processing to the first commerce metrics toform a first set of consumer sentiments, and apply the natural languageprocessing to the second commerce metrics to form a second set ofconsumer sentiments, wherein the commerce metric differences includedifferences between the first set of consumer sentiments and the secondset of consumer sentiments.

Example 25 is the non-transitory computer-readable storage medium ofexample 20, wherein the first commerce metrics include first trafficmetrics, the second commerce metrics include second traffic metrics, andthe instructions, when executed, cause the machine to, wherein thecommerce metric differences include differences between the firsttraffic metrics and the second traffic metrics.

Example 26 is the non-transitory computer-readable storage medium ofexample 25, wherein the first traffic metrics are based on a number offeedback instances and a number of comments.

Example 27 is the non-transitory computer-readable storage medium ofexample 20, wherein the instructions, when executed, cause the machineto, wherein the first commerce metrics include first ratings metrics,and the second commerce metrics include second ratings metrics, whereinthe commerce metric differences include differences between the firstratings metrics and the second ratings metrics.

Example 28 is the non-transitory computer-readable storage medium ofexample 20, wherein the instructions, when executed, cause the machineto identify a statistical gap in a panel composition for a product bycomputing a difference of a first ratio of a first number of sales ofthe product to a panel and a second number of sales of all products tothe panel, and a second ratio of a third number of sales of the productrecorded by eCommerce cooperators and a fourth number of sales of allproducts to the eCommerce cooperators.

Example 29 is a computer-implemented method to model eCommerce salesincluding calculating, by executing an instruction with at least oneprocessor, commerce metric differences corresponding to products, thecommerce metric differences based on first commerce metrics scraped at afirst time and second commerce metrics scraped at a second time,splitting, by executing an instruction with the at least one processor,the commerce metric differences into a first portion of the commercemetric differences corresponding to a first dataset of eCommercecooperators, and into a second portion of the commerce metricdifferences corresponding to a second dataset of eCommercenon-cooperators, estimating infer sales data by estimating, by executingan instruction with the at least one processor, eCommercenon-cooperators sales based on the second portion of the commerce metricdifferences, and estimating, by executing an instruction with the atleast one processor, sales missing from collected sales data based onthe estimated eCommerce non-cooperators sales.

Example 30 is the computer-implemented method of example 29, wherein theestimating the eCommerce non-cooperators sales based on the secondportion of the commerce metric differences includes training a modelusing the first portion of the commerce metric differences, and passingthe second portion of the commerce metric differences through the modelto estimate the eCommerce non-cooperators sales.

Example 31 is the computer-implemented method of example 30, furtherincluding calculating product contribution probability indicescorresponding to the products for (a) the eCommerce cooperators and (b)the eCommerce non-cooperators, the product contribution probabilityindices based on the model.

Example 32 is the computer-implemented method of example 31, furtherincluding calculating wherein the estimating the sales missing fromcollected sales data based on the eCommerce non-cooperators and theproduct contribution probability indices.

Example 33 is the computer-implemented method of example 29, furtherincluding applying natural language processing to the first commercemetrics to form a first set of consumer sentiments, and applying thenatural language processing to the second commerce metrics to form asecond set of consumer sentiments, wherein the commerce metricdifferences include differences between the first set of consumersentiments and the second set of consumer sentiments.

Example 34 is the computer-implemented method of example 29, wherein thefirst commerce metrics include first traffic metrics, and the secondcommerce metrics include second traffic metrics, wherein the commercemetric differences include differences between the first traffic metricsand the second traffic metrics.

Example 35 is the computer-implemented method of example 34, wherein thefirst traffic metrics are based on a number of feedback instances and anumber of comments.

Example 36 is the computer-implemented method of example 29, wherein thefirst commerce metrics include first ratings metrics, and the secondcommerce metrics include second ratings metrics, wherein the commercemetric differences include differences between the first ratings metricsand the second ratings metrics.

Example 37 is the computer-implemented method of example 29, furtherincluding identifying a statistical gap in a panel composition for aproduct by computing a difference of a first ratio of a first number ofsales of the product to a panel and a second number of sales of allproducts to the panel, and a second ratio of a third number of sales ofthe product recorded by eCommerce cooperators and a fourth number ofsales of all products to the eCommerce cooperators.

Example 38 is the computer-implemented method of example 37, wherein thestatistical gap is a first statistical gap in the panel composition fora first product, and further including identifying a second statisticalgap in the panel composition for a second product, and combining thefirst statistical gap and the second statistical gap to identify a thirdstatistical gap in the panel composition related to at least one of astrata, or a consumer class.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or c refers to any combination or subset of A, B, c such as(1) A alone, (2) B alone, (3) c alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

It is noted that this patent claims the benefit of U.S. Provisional Pat.Application Serial No. 62/754,368, entitled “Methods, Systems, Apparatusand Articles of Manufacture to Improve eCommerce Estimation Modeling,”which was filed on Nov. 1, 2018, and is hereby incorporated by referencein its entirety.

Any references, including publications, patent applications, and patentscited herein are hereby incorporated in their entirety by reference tothe same extent as if each reference were individually and specificallyindicated to be incorporated by reference and were set forth in itsentirety herein.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1. (canceled)
 2. A system to model eCommerce sales comprising: salesmodeler circuitry to: split panel sales data into first panel sales datacorresponding to eCommerce cooperators and second panel sales datacorresponding to eCommerce non-cooperators; determine an expansionfactor for a first one of a plurality of strata; and bias reducercircuitry to: determine a first set of weights based on a set ofconsumer profiles and a set of panel profiles, the first set of weightscorresponding to the first one of the plurality of strata, ones of thefirst set of weights to correspond to respective ones of attributes; anddetermine a total unbiased eCommerce non-cooperator sales for a firstproduct and the first one of the plurality of strata based on firstunbiased sales values corresponding to the first product and the firstone of the plurality of strata, ones of the first unbiased sales valuesto correspond to respective ones of the attributes, wherein the ones ofthe first unbiased sales values are based on (a) the expansion factorfor the first one of the plurality of strata, (b) respective ones of thefirst set of weights, and (c) corresponding portions of the second panelsales data.
 3. The system of claim 2, wherein the corresponding portionsof the second panel sales data correspond to the first product, thefirst one of the plurality of strata, and respective ones of theattributes.
 4. The system of claim 2, wherein the first one of theplurality of strata includes one of city, region, socio-economic class,age, and income.
 5. The system of claim 2, wherein, prior to determiningthe expansion factor, the sales modeler circuitry is to: sort the firstpanel sales data, the second panel sales data, and eCommerce cooperatorssales data based on at least one of (a) product category or (b) product;and arrange each of the sorted first panel sales data, the sorted secondpanel sales data, and the sorted eCommerce cooperators sales data intothe plurality of strata.
 6. The system of claim 5, wherein the salesmodeler circuitry is to determine the expansion factor for the first oneof the plurality of strata based on a ratio of a portion of theeCommerce cooperators sales data and a portion of the first panel salesdata, the portions corresponding to the first one of the plurality ofstrata.
 7. The system of claim 5, wherein the bias reducer circuity isto arrange each of (a) the set of consumer profiles and (b) the set ofpanel profiles into the plurality of strata and the attributes.
 8. Thesystem of claim 7, wherein the set of consumer profiles representsunbiased consumer profiles and the set of panel profiles representsbiased consumer profiles, the ones of the first set of weights torepresent a difference between the unbiased consumer profiles for arespective attribute and the biased consumer profiles for the respectiveattribute.
 9. The system of claim 5, wherein the bias reducer circuityis to determine total unbiased eCommerce sales for the first product andthe first one of the plurality of strata across the eCommercecooperators and the eCommerce non-cooperators based on (a) the totalunbiased eCommerce non-cooperators sales for the first product and thefirst one of the plurality of strata and (b) a portion of the eCommercesales data corresponding to the first product and the first one of theplurality of strata.
 10. The system of claim 9, wherein the salesmodeler circuitry is to determine a plurality of expansion factors, onesof the plurality of expansion factors to correspond to respective onesof the plurality of strata; and wherein the bias reducer circuity is to:determine a plurality of sets of weights, ones of the plurality of setsof weights to correspond to respective ones of the plurality of strata;determine a plurality of total unbiased eCommerce non-cooperator salesfor the first product, ones of the plurality of total unbiased eCommercenon-cooperator sales to correspond to respective ones of the pluralityof strata; and determine total unbiased eCommerce sales for the firstproduct across (a) the plurality of strata, (b) the eCommercecooperators and (c) the eCommerce non-cooperators.
 11. The system ofclaim 10, wherein the bias reducer circuitry is to determine totaleCommerce sales across products by determining total eCommerce sales foreach of the products, the products including the first product.
 12. Atleast one non-transitory computer readable storage medium comprisinginstructions that, when executed, cause processor circuitry to at least:split panel sales data into first panel sales data and second panelsales data, the first panel sales data corresponding to eCommercecooperators, the second panel sales data corresponding to eCommercenon-cooperators; determine an expansion factor for a stratum; determinea group of weights based on a set of consumer profiles and a set ofpanel profiles, the group of weights to correspond to the stratum, onesof the group of weights to correspond to respective ones of attributes;determine a group of unbiased sales values corresponding to eCommercenon-cooperators, the group of unbiased sales values to correspond to aproduct and the stratum, ones of the group of unbiased sales valuesdetermined based on (a) the expansion factor for the stratum, (b)respective ones of the group of weights, and (c) corresponding portionsof the second panel sales data; and determine total unbiased eCommercenon-cooperator sales for the product and the stratum, the total unbiasedeCommerce non-cooperators sales for the product and the stratum based onthe group of unbiased sales values.
 13. The at least one non-transitorycomputer readable storage medium of claim 12, wherein the correspondingportions of the second panel sales data correspond to the product, thestratum, and respective ones of the attributes.
 14. The at least onenon-transitory computer readable storage medium of claim 12, wherein thestratum is one of a set of strata, and wherein, prior to determining theexpansion factor, the instructions, when executed, cause the processorcircuitry to: sort the first panel sales data, the second panel salesdata, and eCommerce cooperators sales data based on at least one of (a)product category or (b) by product; and arrange each of the sorted firstpanel sales data, the sorted second panel sales data, and the sortedeCommerce cooperators sales data into the set of strata.
 15. The atleast one non-transitory computer readable storage medium of claim 14,wherein the instructions, when executed, cause the processor circuitryto determine the expansion factor for the stratum based on a ratio of aportion of the eCommerce cooperators sales data and a portion of thefirst panel sales data, the portions corresponding to the stratum. 16.The at least one non-transitory computer readable storage medium ofclaim 14, wherein the instructions, when executed, cause the processorcircuitry to arrange each of (a) the set of consumer profiles and (b)the set of panel profiles into the set of strata and the attributes. 17.The at least one non-transitory computer readable storage medium ofclaim 16, wherein the set of consumer profiles represents unbiasedconsumer profiles and the set of panel profiles represents biasedconsumer profiles, the ones of the group of weights to represent adifference between the unbiased consumer profiles for a respectiveattribute and the biased consumer profiles for the respective attribute.18. The at least one non-transitory computer readable storage medium ofclaim 14, wherein the instructions, when executed, cause the processorcircuitry to determine total unbiased eCommerce sales for the productand the stratum based on the total unbiased eCommerce non-cooperatorssales for the product and the stratum and a portion of the eCommercesales data corresponding to the product and the stratum.
 19. The atleast one non-transitory computer readable storage medium of claim 18,wherein the instructions, when executed, cause the processor circuitryto: determine a plurality of expansion factors corresponding to the setof strata, ones of the expansion factors to be based on respective onesof the set of strata; determine groups of weights, ones of the groups ofweights to correspond to respective ones of the set of strata; determinegroups of unbiased sales values, ones of the groups of unbiased salesvalues to correspond to the product and respective ones of the set ofstrata, the ones of the groups of unbiased sales values determined basedon (a) respective ones of the expansion factors, (b) respective weightsof respective ones of the groups of weights, and (c) correspondingportions of the second panel sales data; and determine a plurality oftotal unbiased eCommerce non-cooperator sales for the product, ones ofthe plurality of total unbiased eCommerce non-cooperator sales tocorrespond to respective ones of the set of strata.
 20. The at least onenon-transitory computer readable storage medium of claim 19, wherein theinstructions, when executed, cause the processor circuitry to determinetotal unbiased eCommerce sales for the product across the set of strata,across the eCommerce cooperators, and across the eCommercenon-cooperators, wherein the total unbiased eCommerce sales for theproduct is based on (a) the ones of the plurality of total unbiasedeCommerce non-cooperator sales and (b) respective portions of theeCommerce sales data.
 21. A method comprising: splitting, by executingmachine readable instructions with at least one processor, panel salesdata into first panel sales data corresponding to eCommerce cooperatorsand second panel sales data corresponding to eCommerce non-cooperators;determining, by executing the machine readable instructions with the atleast one processor, expansion factors for strata of interest, ones ofthe expansion factors corresponding to respective ones of the strata;determining, by executing the machine readable instructions with the atleast one processor, weights for the ones of the strata based on a setof consumer profiles and a set of panel profiles, wherein first weightsof the weights correspond to a first stratum, and wherein ones of thefirst weights correspond to respective ones of attributes; determining,by executing the machine readable instructions with the at least oneprocessor, unbiased sales values for a product of interest, the unbiasedsales values corresponding to eCommerce non-cooperators, ones of theunbiased sales values to correspond to respective ones of the strata,wherein a first one of the unbiased sales values for the first stratumis based on (a) a respective one of the expansion factors, (b) the firstweights, and (c) corresponding portions of the second panel sales data;determining, by executing the machine readable instructions with the atleast one processor, eCommerce sales values for the product of interest,ones of the eCommerce sales values to correspond to respective ones ofthe strata, wherein a first one of the eCommerce sales valuescorresponding to the first stratum is based on the unbiased sales valuesand a respective portion of eCommerce cooperators sales data; anddetermine a total unbiased eCommerce sales for the product of interestacross the strata of interest based on the eCommerce sales values forthe product of interest.
 22. The method of claim 21, further includingdetermining total eCommerce sales for a plurality of products, the totaleCommerce sales for the plurality of products based on a respectiveplurality of total eCommerce sales, ones of the plurality of totaleCommerce sales corresponding to respective ones of the plurality ofproducts.